EDGE-VPP is presented, an end-to-end scheduling framework that connects fine-grained load perception with safety-aware decision-making across multiple temporal scales and achieves the lowest operating cost and the fewest constraint violations among the evaluated scheduling methods.
Abstract
The proliferation of distributed energy resources at the edge of distribution networks provides substantial flexibility for virtual power plant (VPP) operation. However, existing methods often rely on aggregate load information and homogeneous scheduling policies. They, therefore, overlook device-specific response characteristics, heterogeneous response times, and operational safety constraints. This paper presents EDGE-VPP, an end-to-end scheduling framework that connects fine-grained load perception with safety-aware decision-making across multiple temporal scales. At the perception layer, a Load Decomposition Transformer (LDT) uses learnable multi-frequency positional encodings and device-specific attention heads. It jointly detects appliance states and disaggregates device power from aggregate measurements. At the coordination layer, a three-tier cloud–edge–device architecture assigns sub-second emergency response to devices, minute-level economic dispatch to edge controllers, and hour-ahead planning to the cloud. Bidirectional information exchange mitigates conflicts among these control layers. At the optimization layer, multi-constraint proximal policy optimization factorizes continuous and discrete actions. Adaptive Lagrange multipliers enforce voltage and current limits, while two value estimators stabilize policy learning. Experiments on REDD, UK-DALE, and a self-constructed VPP dataset show that LDT reduces mean absolute error by up to 6.86% and improves the F1-score by 3.51% over the Transformer baseline. The complete EDGE-VPP framework also achieves the lowest operating cost and the fewest constraint violations among the evaluated scheduling methods.
High penetrations of distributed energy resources require energy regulation that combines cloud-level global optimization with edge-level fast response. This paper proposes EC-HDT, a device-edge-cloud hierarchical digital twin in which a lightweight graph-attention-temporal-convolution estimator reconstructs local states under asynchronous, noisy, and missing measurements, while a cloud predictor and model predictive controller perform rolling economic optimization. A five-factor decision weight based on communication latency, information freshness, estimation confidence, operational risk, and edge computational load continuously allocates control authority between edge and cloud, and a quadratic-programming safety layer enforces physical constraints. On the IEEE 33-bus system, EC-HDT achieves a nodal-voltage MAE of 0.0076 p.u., mean/P95 end-to-end latencies of 56.4/89.4 ms, and a 99.2% control success rate; the daily operating cost is 3.51% lower than that of the fixed-fusion scheme. The results indicate that state-aware edge-cloud coordination can improve the latency-economy-safety trade-off in distribution-system regulation.
This paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control that integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization, a three-time-scale edge–cloud architecture, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy.
Shichao Huang, Yi-Bing Zhou, Yuan Liu· Italian National Conference...· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
The rapid expansion of distribution networks and the increasing complexity of their topological structures pose significant challenges to fast and reliable post-fault service restoration. Meanwhile, driven by carbon neutrality goals, the large-scale integration of distributed energy resources (DERs) enhances operational flexibility but also introduces pronounced intermittency and uncertainty, further complicating post-fault load transfer decision-making. To address these challenges, this paper proposes an expert-guided and virtual power plant (VPP)-assisted load transfer optimization framework based on hierarchical graph reinforcement learning. A topology-aware graph neural network (GNN)–based state representation is developed, in which buses are modeled as nodes and switches as controllable edges, enabling explicit modeling of network connectivity and electrical coupling. On this basis, a hierarchical decision-making architecture is constructed: the upper-level agent, guided by expert knowledge, dynamically selects the restoration task type to coordinate the timing of network reconfiguration and VPP-assisted DER regulation; driven by this high-level directive, two specialized lower-level agents respectively execute the specific switch operations and stepwise DER power adjustments, ensuring power balance and voltage security. Simulation results on a practical distribution network demonstrate that, under high DER penetration, the proposed method achieves faster service restoration, higher load recovery ratios, and significantly fewer voltage violation events than conventional reinforcement learning approaches, exhibiting improved operational safety and scheduling stability.
Lu Chen, Jinhu Fang, Xiaona Lv et al.· PLoS ONE· 0 citations
A deep reinforcement learning (DRL) framework that jointly co-schedules computing and thermal resources so that a hyperscale data center can operate as a grid-interactive flexible load and supports the evolution of hyperscale data centers from passive electricity consumers toward active, grid-interactive participants in renewable-penetrated power systems.
As intermittent renewables increasingly penetrate power systems, virtual power plants (VPPs) have emerged as a critical component of power systems, aggregating geographically distributed devices to respond to price signals and mitigate supply-demand imbalances. Consequently, coordinating heterogeneous resources across hierarchical multi-region pricing to fulfill committed bids while maximizing arbitrage has become a critical challenge. Existing operations-research and reinforcement-learning approaches rely on handcrafted formulations or learned policies that struggle to scale or lack interpretability, limiting trustworthiness in safety-critical energy systems. In contrast, large language models enable evolving interpretable and effective optimization by reasoning over structured decisions and generating executable programs. Building on this paradigm, we propose VPPEvolve, a reasoning-guided evolutionary framework for hierarchical VPP scheduling. VPPEvolve tackles three key challenges through three complementary designs: (i) an evolvable chained program representation that formalizes hierarchical VPP dynamics through executable structures; (ii) a spatio-temporal profiling and reflection module that bridges the semantic gap between volatile numerical signals and structured reasoning space; and (iii) a device attribution-aware inspiration module that enables LLM-informed evolution by explicitly attributing device-level contributions to improve heterogeneous coordination. Extensive experiments on two city-scale datasets confirm consistent gains in economic profit and bid-tracking stability, with post-hoc analyses and deployment confirming a white-box paradigm that reduces grid-side stress. Codes and data are available at: https://github.com/JinweiZzz/VPPEvolve.
Jinwei Zeng, Guozhen Zhang, Minbo Ma et al.· Proceedings of the 32nd ACM...· 0 citations
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